Information Processing Apparatus

The present invention is based upon and claims the benefit of priority from Japanese patent application No. 2025-035398, filed on Mar. 6, 2025, the disclosure of which is incorporated herein in its entirety by reference.

The present invention relates to an information processing apparatus, an information processing method, and a storage medium.

Techniques used to integrate a plurality of observation results acquired using a plurality of observation nodes such as security cameras are known.

As a related document, Patent Literature 1 is known. Patent Literature 1 describes a subarea monitoring device having an integration processing unit that integrates monitoring results obtained from a first subarea monitoring unit and a second subarea monitoring unit to generate a monitoring result for the entire subarea.

Patent Literature 1: JP 2019-153986 A

When there are a plurality of observation nodes, integration processing may be distributed to a plurality of devices. In this case, depending on how the observation nodes are divided, the load may be unevenly distributed among specific devices, making it difficult to perform efficient integration processing. As described above, in the case of performing integration processing distributed among a plurality of devices, there is a problem that it is sometimes difficult to achieve appropriate distribution.

Therefore, an example object of the present disclosure is to provide an information processing apparatus, an information processing method, and a program that can solve the aforementioned problem.

In order to achieve such an example object, an information processing apparatus according to the present disclosure is configured to include

an acquiring unit that acquires load information indicating a load corresponding to each of observation nodes that observes an object, and

an allocation unit that, with use of the acquired load information, allocates observation nodes that perform observations subject to integration processing to a plurality of integration processing nodes that perform integration processing to integrate observation results by the observation nodes.

Further, an information processing method according to the present disclosure is configured to include, by an information processing apparatus,

acquiring load information indicating a load corresponding to each of observation nodes that observes an object, and

with use of the acquired load information, allocating observation nodes that perform observations subject to integration processing to a plurality of integration processing nodes that perform integration processing to integrate observation results by the observation nodes.

Further, a storage medium according to the present disclosure is a computer-readable storage medium storing thereon a program for causing an information processing apparatus to execute processing to:

acquire load information indicating a load corresponding to each of observation nodes that observes an object; and

with use of the acquired load information, allocate observation nodes that perform observations subject to integration processing to a plurality of integration processing nodes that perform integration processing to integrate observation results by the observation nodes.

According to the above-described configurations, in the case of performing integration processing distributed among a plurality of devices, distribution can be performed more appropriately.

100 100 200 241 242 243 1 12 FIGS.to 1 FIG. 2 FIG. 3 FIG. 4 FIG. 5 FIG. 6 FIG. 7 FIG. 8 10 FIGS.to 11 12 FIGS.and An example configuration of a data integration systemin the present disclosure will be described with reference to.illustrates an overview of integration processing.illustrates an example configuration of the data integration system.is a block diagram illustrating an example configuration of a control device.illustrates an example of positional relationship information.illustrates an example of load information.illustrates an example of processing time information.illustrates an example of weighting processing.illustrate an example of allocation processing.are flowcharts illustrating an example of operation during integration processing. In the present disclosure, the drawings may be associated with one or a plurality of example embodiments.

100 110 150 100 242 110 100 242 110 100 242 120 110 100 110 242 100 242 110 100 110 110 100 150 110 150 100 150 150 The present disclosure describes the data integration systemin which integration processing for integrating observation results by a plurality of observation nodesincluded in the system is distributed among a plurality of integration processing nodes. For example, the data integration systemacquires load informationindicating the load corresponding to each observation nodethat observes an object. As an example, the data integration systemacquires the load informationthat includes at least one of the number of observations of the object previously observed by each observation nodeand the amount of movement indicating the amount by which the object has moved from the observation area, as information indicating the load during the integration processing. The data integration systemmay acquire and store the load informationin advance by acquiring information from an analysis devicethat analyzes the observation results of the observation nodes, or the like. The data integration systemalso allocates the observation nodesusing the load information. For example, the data integration systemuses the load informationto apply a weight to each observation node. Furthermore, the data integration systemdivides the observation nodesinto a plurality of groups using the weighting results and information indicating the positional relationship of the observation nodes. Thereafter, the data integration systemdetermines an integration processing noderesponsible for each group, and allocates the observation nodessubject to integration processing to each integration processing node. As an example, the data integration systemmay perform allocation so that the load is distributed among the integration processing nodesby, for example, distributing the weight values among the integration processing nodes.

100 242 242 242 100 100 100 150 Furthermore, the data integration systemis able to perform allocation for a predetermined number of days in advance. For example, the load informationcontains information indicating the load during integration processing for each predetermined time period such as every hour, for one week. The load informationmay include the above information for each weather type, such as rainy days and cloudy days. By using the load information, the data integration systemis able to perform, for example, hourly allocation for one week in advance. In this case, the data integration systemmay acquire information indicating the weather forecast from an external device or the like and perform allocation using the acquired information. Furthermore, the data integration systemcan instruct each integration processing nodeto execute integration processing according to the results of allocation each time the allocated time arrives.

110 100 110 110 110 110 The observation nodemay be at least one of a security camera, an infrared sensor, and any other sensor, or a combination thereof. The data integration systemmay include a plurality of types of observation nodes. Furthermore, an object observed by the observation nodemay be a person, a mobile device such as a drone or an automated guided vehicle, or any other object. For example, the observation nodeobserves an object with the purpose of reproducing the observation results in a digital space in an arbitrary digital twin system or the like. The observation nodemay observe an object for purposes other than those mentioned above.

150 100 110 110 150 150 1 2 110 120 110 100 110 150 100 1 FIG. 1 FIG. In the present disclosure, the integration processing nodeincluded in the data integration systemperforms, as integration processing, processing to recognize an object such as a person who moves across observation areas observed by the observation nodesas a unique object. For example, for each of a pair of adjacent observation nodes, the integration processing nodeacquires person information or the like that can be acquired by analyzing the observation result for a predetermined period of time, such as one hour. The integration processing nodethen compares the acquired pieces of information to recognize a person or the like moving across the observation areas as a unique object. For example,illustrates an example of integration processing in the case where observation results of a cameraand a camera, which are observation nodes, are to be integrated. Referring to, in the stage where the integration processing is not performed, the analysis deviceperforms separate recognition using the observation result of each observation node. Therefore, the data integration systemrecognizes objects that move across the observation areas of the observation nodesas separate objects such as a person A and a person B. On the other hand, when the integration processing nodeperforms integration using the time of passing through the observation area, it is possible to appropriately recognize an object such as a person moving across the observation areas. As a result, the data integration systemcan appropriately recognize persons who move across the observation areas as the same person.

2 FIG. 2 FIG. 2 FIG. 100 100 110 120 130 140 150 200 110 120 120 130 130 150 150 140 200 150 illustrates an example configuration of the data integration system. Referring to, the data integration systemincludes a plurality of observation nodes, the analysis device, an analysis data storage device, an integrated data storage device, the integration processing node, and a control device. As illustrated in, the observation nodeand the analysis devicecan be communicably connected to each other in a wired or wireless manner. The analysis deviceand the analysis data storage devicecan be communicably connected to each other in a wired or wireless manner. The analysis data storage deviceand the integration processing nodecan be communicably connected to each other in a wired or wireless manner. The integration processing nodeand the integrated data storage devicecan be communicably connected to each other in a wired or wireless manner. The control deviceand the integration processing nodecan be communicably connected to each other in a wired or wireless manner.

2 FIG. 2 FIG. 2 FIG. 2 FIG. 100 100 100 100 100 120 130 Note thatillustrates an example configuration of the data integration system. The configuration of the data integration systemis not limited to the example illustrated in. For example, respective components included in the data integration systemmay be connected via a network or the like. The number of components included in the data integration systemmay be other than that exemplified in. The data integration systemmay have a configuration other than that exemplified in, such as a configuration in which the analysis deviceand the analysis data storage deviceare integrated.

110 110 110 110 The observation nodeis a device that observes an object such as a person in a predetermined observation area. The observation nodemay be at least one of a security camera, an infrared sensor, and any other sensor, or a combination thereof. For example, when the observation nodeis a security camera, the observation nodecan acquire time-series surveillance image data as an observation result.

120 110 120 110 120 120 110 120 110 The analysis deviceis a device that analyzes the observation results from the observation nodesand acquires person information and the like. The analysis devicecan use the observation results of the observation nodeto perform object detection processing to detect an object such as a person, and tracking processing to track the detected object within the observation area. In addition, by using the results of object detection processing and tracking processing, the analysis devicecan measure the number of objects such as persons within the observation area as the number of observations, and measure the amount of movement that indicates the amount by which the object has moved from the observation area. The analysis devicemay perform the above-described analysis processing using a model that has been trained in advance. Furthermore, when the observation nodeis an infrared sensor or the like, the analysis devicemay use information acquired from the observation nodeto measure the number of observations, for example.

120 110 110 120 110 120 130 130 For example, the analysis deviceperforms the analysis processing for each observation nodeusing the observation result of each observation node. In other words, the analysis deviceperforms analysis processing individually using the observation result by each observation node. Furthermore, the analysis devicecan store the results of the analysis processing in the analysis data storage deviceor the like. The analysis data storage devicemay be any storage device including a hard disk, memory, and the like.

150 130 200 150 110 200 150 110 110 200 150 140 140 The integration processing nodeperforms the above-described integration processing using information acquired from the analysis data storage device, and the like. In response to an instruction from the control device, the integration processing nodeperforms integration processing on the observation results of the observation nodesallocated by the control device. In other words, the integration processing nodemay perform integration processing on each of the integration objects that can be identified by the instruction, such as a pair of adjacent observation nodesamong the observation nodesincluded in the instruction from the control device. Furthermore, the integration processing nodecan store the results of the integration processing in the integrated data storage deviceor the like. The integrated data storage devicemay be any storage device including a hard disk, memory, and the like.

150 200 150 200 After the integration processing is completed, the integration processing nodecan notify the control devicethat the integration processing has been completed. At this time, the integration processing nodemay transmit information indicating the time taken for the integration processing, to the control device.

200 110 150 242 200 200 150 200 200 210 220 230 240 250 3 FIG. 3 FIG. The control deviceis an information processing apparatus that allocates observation nodesto each of a plurality of integration processing nodesusing previously acquired load informationand the like. The control devicemay perform allocation for each predetermined condition, such as for each predetermined time, each day of the week, each weather, or the like. The control deviceis also able to instruct each integration processing nodeto execute integration processing according to the allocation.illustrates an example of the main components of the control device. Referring to, the control deviceincludes, as its main components, an operation input unit, a screen display unit, a communication interface unit, a memory unit, and an arithmetic processing unit.

3 FIG. 200 200 200 200 210 220 illustrates an example in which the functions of the control deviceare realized using one information processing apparatus. However, the control devicemay be realized using a plurality of information processing apparatus, such as at least a part of the functions as the control devicebeing realized on the cloud, for example. Furthermore, the control devicemay not include some of the components exemplified above, such as not having the operation input unitor the screen display unit, or may have components other than those exemplified above.

210 210 200 250 The operation input unitis configured of operation input devices such as a keyboard and a mouse. The operation input unitdetects an operation by a user who operates the control deviceand outputs the detected operation to the arithmetic processing unit.

220 220 240 250 The screen display unitis configured of a screen display device such as a liquid crystal display or an organic electroluminescence (EL) display. The screen display unitcan display various types of information stored in the memory uniton the screen in response to instructions from the arithmetic processing unit.

230 230 150 The communication interface unitincludes a data communication circuit and the like. The communication interface unitperforms data communication with an external device such as the integration processing nodeconnected via a communication line.

240 240 244 250 244 250 244 230 240 240 241 242 243 The memory unitis a storage device such as a hard disk or a memory. The memory unitstores processing information and programsrequired for various types of processing by the arithmetic processing unit. The programsare read into the arithmetic processing unitand executed to realize various processing units. The programsare read in advance from an external device or a storage medium via a data input/output function such as the communication interface unitand is stored in the memory unit. The main information stored in the memory unitincludes, for example, positional relationship information, the load information, processing time information, and the like.

241 110 241 210 230 240 The positional relationship informationincludes information indicating the positional relationship of the observation nodessuch as security cameras and sensors. The positional relationship informationcan be acquired in advance by accepting input using the operation input unitor by receiving information from an external device via the communication interface unit, and can be stored in the memory unit.

4 FIG. 4 FIG. 4 FIG. 4 FIG. 241 241 110 241 1 2 illustrates an example of the positional relationship information. Referring to, the positional relationship informationcan have information indicating the positional relationship of the observation nodesas graph information including information indicating positional relationships such as up/down, left/right, and height. For example, in the case of, the positional relationship informationindicates that the cameraand the cameraare adjacent to each other. The positional relationship information 241 may be other than that illustrated in.

242 110 242 242 256 242 210 230 240 The load informationincludes information indicating the load corresponding to each observation node. For example, the load informationmay include information indicating the load during integration processing for each predetermined condition. The load informationcan be updated in response to the load information storing unitstoring information, for example. The load informationmay be acquired in advance by accepting input using the operation input unitor by receiving information from an external device via the communication interface unit, and may be stored in the memory unit.

5 FIG. 5 FIG. 5 FIG. 242 242 110 242 110 illustrates an example of the load information. Referring to, the load informationincludes load data that is information indicating the load during integration processing for each observation node, for each condition such as a time period or day of the week. For example, in the the case of, the load informationincludes load data for each hour for each day of the week. Here, the information indicating the load during the integration processing may include at least one of the number of observations of the object previously observed at the associated observation nodeand the amount of movement indicating the amount by which the object has moved from the observation area. The number of observations or the amount of movement may include a plurality of past values, or may include a statistical value such as a total or an average. The information indicating the load during the integration processing may include information other than the aforementioned examples.

242 242 242 242 The load informationmay include information other than those illustrated above. For example, the load informationmay include the aforementioned various types of information for each weather type, such as rainy, sunny, and cloudy. The load informationmay include the aforementioned various types of information for each predetermined event in the observation area or its surroundings. In this way, the load informationcan include load data and the like for each of various conditions under which the load during the integration processing may fluctuate.

243 243 242 243 255 The processing time informationincludes information indicating the time taken in the past integration processing. The processing time informationmay include information indicating the aforementioned time for each of the conditions similar to the conditions included in the load information. The processing time informationcan be updated by the processing time storing unitstoring information.

6 FIG. 6 FIG. 6 FIG. 243 243 243 150 243 illustrates an example of the processing time information. Referring to, the processing time informationincludes processing time data indicating the time taken for the integration processing for each condition such as a time period or day of the week. For example, in the case of, the processing time informationincludes processing time data for each hour for each day of the week. Here, the processing time data may include information indicating the time taken for the integration processing for each integration processing node. In other words, the processing time data can include processing time data for each of the groups in which processing is performed under the same conditions, such as the same time period. The processing time informationmay include information other than that illustrated above.

250 250 244 240 244 250 251 252 253 254 255 256 The arithmetic processing unitincludes an arithmetic logic unit such as a CPU (Central Processing Unit) and its peripheral circuits. The arithmetic processing unitreads and executes the programfrom the memory unit, thereby causing the above hardware and the programto cooperate to realize various processing units. The main processing units realized by the arithmetic processing unitinclude, for example, an acquiring unit, a weighting unit, an allocation unit, an instruction unit, the processing time storing unit, and the load information storing unit.

250 Note that the arithmetic processing unitmay have a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), an MPU (Micro Processing Unit), an FPU (Floating point number Processing Unit), a PPU (Physics Processing Unit), a TPU (Tensor Processing Unit), a quantum processor, a microcontroller, or a combination thereof, instead of the above-described CPU.

251 252 253 110 The acquiring unit, the weighting unit, and the allocation unitfunction as a planning unit that allocates the observation nodes. The planning unit can be activated at predetermined intervals, such as once a week, to make allocation for a predetermined number of days.

251 240 241 242 The acquiring unitrefers to the memory unitand acquires information included in the positional relationship information, the load information, and the like.

251 251 242 251 242 251 241 The acquiring unitis able to acquire at least one of weather forecast information, information indicating presence/absence of a predetermined event, and the like from an external device or the like. The acquiring unitmay acquire part of the information included in the load informationand the like using the acquired weather forecast information and the like. For example, the acquiring unitis able to acquire, from the information included in the load information, load data that satisfies the conditions such as weather for each time period for which allocation is performed. The acquiring unitalso acquires the positional relationship information.

252 242 251 252 252 241 The weighting unituses the load informationacquired by the acquiring unitto apply a weight to each observation node. For example, the weighting unitis able to perform weighting using the corresponding load data for each condition such as day of the week or a time period for which allocation is performed. When performing weighting, the weighting unitmay use the positional relationship informationin addition to the above information.

252 252 252 252 242 252 For example, the weighting unitperforms weighting such that the greater the number of observations, the greater the weight. The weighting unitalso performs weighting such that the greater the amount of movement, the greater the weight. As an example, the weighting unitmay perform weighting while giving more importance to the amount of movement than the number of observations. For example, the weighting unitperforms weighting using the number of observations and the amount of movement included in the load informationas described above. Note that a specific calculation formula used by the weighting unitto calculate the weight may be set arbitrarily.

110 242 110 252 110 110 110 252 241 110 110 252 110 110 252 110 252 241 1 1 110 6 110 252 80 1 55 1 6 110 7 FIG. 7 FIG. When a new observation nodeis added or the like, there may be a case where the load informationdoes not contain information corresponding to the added observation node. In such a case, the weighting unitmay calculate the weight of the newly added observation nodeusing the weight of the observation nodeadjacent to the newly added observation node. For example, the weighting unitrefers to the positional relationship informationto identify the observation nodesadjacent to the newly added observation node. Then, the weighting unitis able to calculate the weight of the newly added observation nodeby calculating the average value of the weights of the identified observation nodes.illustrates an example of weighting by the weighting unitwhen a new observation nodeis added. Referring to, the weighting unitrefers to the positional relationship informationto identify the cameraand a sensoras the observation nodesadjacent to a camerathat is a newly added observation node. Then, the weighting unitcalculates the average of the weight "" of the identified cameraand the weight "" of the identified sensorto thereby calculate the weight of the camerathat is the newly added observation nodeto be "67.5".

253 252 110 150 253 110 150 252 242 253 110 242 The allocation unituses the weighting result from the weighting unitto allocate the observation nodesto each of a plurality of the integration processing nodesfor each condition such as day of the week or a time period. The allocation unitmay perform graph partitioning to allocate the observation nodesto each of the plurality of integration processing nodesusing any algorithm. The weighting unitperforms weighting using the load informationas described above. Therefore, it can be said that the allocation unitallocates the observation nodesusing the load information.

253 241 110 253 110 253 110 150 110 253 110 253 110 110 110 110 253 253 110 110 253 110 150 150 253 For example, the allocation unituses the weighting results and the positional relationship informationto divide the observation nodesinto groups according to the number of the integration processing nodes. As an example, the allocation unituses the weighting result to select a predetermined number of observation nodesas leader nodes. The allocation unitmay select, as leader nodes, a number of observation nodesequal to the number of integration processing nodesincluded in the system, in order from the observation nodewith the largest weight. At this time, it is desirable that the allocation unitselects a leader node so that a predetermined condition is met, such as presence of an adjacent observation nodethat is not a leader node, so that subsequent grouping is possible. Also, the allocation unitallocates the observation nodesincluded in the system to each selected observation nodethat is a leader node by, for example, grouping adjacent observation nodesstarting from the selected observation nodethat is a leader node. At this time, it is desirable that the allocation unitperforms the allocation so that the total weight values in the respective groups are somewhat equal. For example, by using the method described above, the allocation unitdivides the observation nodesincluded in the system into a plurality of groups, each group consisting of adjacent observation nodes. Thereafter, the allocation unitis able to allocate the observation nodesto each integration processing nodeby determining which integration processing nodewill be in charge of each group. The allocation unitmay be configured to reselect a leader node when it is determined that an imbalance has occurred between groups due to the allocation described above.

8 FIG. 8 FIG. 8 FIG. 8 FIG. 253 150 253 110 110 1 3 4 253 253 110 253 150 110 150 illustrates an example of allocation by the allocation unitwhen the number of integration processing nodesis three. Referring to, the allocation unitselects three observation nodesas leader nodes in descending order of weight so that grouping is possible. For example, in the case of, three observation nodes, that is, the camera, the camera, and the camera, are selected as leader nodes. Thereafter, the allocation unitgroups the nodes starting from the selected three leader nodes. As a result, the allocation unitis able to divide the observation nodesinto three groups, as illustrated in. Furthermore, the allocation unitdetermines the integration processing noderesponsible for each group, so that it is possible to allocate the observation nodesto each integration processing node.

253 150 253 150 253 150 241 150-1 150-2. 253 253 150 253 1 2 2 3 150 9 FIG. 9 FIG. 9 FIG. The allocation unitis also able to allocate integration processing between groups to each integration processing node. The allocation unitmay allocate integration processing between groups to each integration processing nodeusing any method. For example, as illustrated in, the allocation unitis able to allocate integration processing to the same integration processing nodefor groups that are close to each other in the positional relationship informationor the like. For example, in the case of, integration processing between the left group and the center group is allocated to an integration processing node, and integration processing between the center group and the right group is allocated to an integration processing nodeThe allocation unitmay be configured to allocate more finely using weights or the like. For example, the allocation unitmay perform allocation using weights or the like so that the loads during integration processing on the respective integration processing nodesbecome as equal as possible. As an example, in the case of, the allocation unitmay allocate integration processing between the sensorand the sensor, integration processing between the cameraand a sensor, and the like, to be performed by different integration processing nodes.

242 110 252 253 241 110 110 253 253 110 253 242 10 FIG. 10 FIG. When the load informationcontains insufficient information in the initial state or the like, it may be difficult to properly calculate the weight of each observation node. When it is difficult for the weighting unitto calculate appropriate weights as described above, the allocation unitmay use the positional relationship informationto divide respective observation nodesinto a plurality of groups so that adjacent observation nodesand the number of nodes become equal in the respective groups.illustrates an example of allocation by the allocation unitwhen the weights cannot be calculated. For example, in the case of, the allocation unitperforms allocation so that the number of observation nodesin each group becomes three or four. Even in such a case, the allocation unitmay perform allocation using weights when it determines that the information included in the load informationsatisfies the conditions and that appropriate weights can be calculated.

253 243 150 243 253 110 253 110 243 The allocation unitmay also perform the above-described allocation in consideration of the processing time information. For example, for an integration processing nodewhose processing time data in the processing time informationexceeds a predetermined value, the allocation unitmay reduce the number of observation nodesincluded in the group or adjust the grouping so that the total weight value becomes smaller. As an example, the allocation unitmay be configured to adjust the observation nodesallocated to each group using the processing time informationafter performing allocation using weights in the above-described manner.

251 252 253 254 253 240 The planning unit can be configured of the acquiring unit, the weighting unit, the allocation unit, and the like, as explained above. The processing by the planning unit and the processing by the instruction unitand subsequent units do not necessarily have to be performed consecutively. For example, the allocation unitconstituting the planning unit may perform allocation for a predetermined number of days, such as one week, and then store information indicating the results of the allocation in the memory unitor the like.

254 253 150 254 150 110 254 150 The instruction unituses the results of allocation by the allocation unitto instruct each integration processing nodeto perform integration processing. For example, the instruction unitmay transmit, to the integration processing node, an instruction including information about the observation nodesor groups subject to integration processing. For example, the instruction unitis able to refer to a clock function or the like to instruct each integration processing nodeto execute integration processing each time a pre-allocated time arrives.

150 255 240 243 150 200 After completion of the integration processing by the integration processing node, the processing time storing unitstores information indicating the time taken for the integration processing in the memory unitas the processing time information. The time taken for the integration processing may be measured on the integration processing nodeside or on the control deviceside.

256 110 256 150 256 240 242 The load information storing unitacquires information indicating the load corresponding to each observation nodeduring the time period in which the integration processing is performed, such as the number of observations and the amount of movement. For example, the load information storing unitmay acquire the aforementioned information via the integration processing node. Furthermore, the load information storing unitis able to store the acquired information in the memory unitas the load information.

200 11 12 FIGS.and 11 FIG. The above is an example configuration of the control device. Next, an example of operation during the integration processing will be described with reference to. First, an example of the overall operation during the integration processing will be described with reference to.

11 FIG. 11 FIG. 251 252 253 101 is a flowchart illustrating an example of operation during the integration processing. Referring to, the planning unit that is configured of the acquiring unit, the weighting unit, and the allocation unitallocates integration processing (step S).

254 253 150 254 150 150 102 The instruction unituses the results of allocation by the allocation unitto instruct each integration processing nodeto perform integration processing. For example, the instruction unitrefers to the clock function or the like and, when the pre-allocated time arrives, instructs each integration processing nodeto execute the integration processing. As a result, the integration processing nodeperforms integration processing on the instructed integration targets (step S).

150 255 240 243 103 255 150 After completion of the integration processing by the integration processing node, the processing time storing unitstores information indicating the time taken for the integration processing in the memory unitas the processing time information(step S). The processing time storing unitmay store information for each integration processing nodeor for each group.

256 110 256 150 256 240 242 104 The load information storing unitacquires information indicating the load corresponding to each observation nodein the time period in which the integration process is performed, such as the number of observations and the amount of movement. For example, the load information storing unitmay acquire the aforementioned information via the integration processing node. Furthermore, the load information storing unitis able to store the acquired information in the memory unitas the load information(step S).

105 254 150 102 When the entire period for which allocation is made has not elapsed (step S, NO), the instruction unitrefers to the clock function or the like, and when the next time for which allocation is made arrives, instructs each integration processing nodeto execute integration processing. Thereby, the processing returns to step S.

105 101 101 On the other hand, when the entire period for which allocation is made has elapsed (step S, YES), the processing returns to step S, and the planning unit performs the processing of step S.

11 FIG. 12 FIG. 101 102 101 The above is an example of operation during the integration processing. In the processing illustrated in, the processing of step Sis not necessarily performed consecutively with the processing of step Sand subsequent steps. Next, the processing of step Swill be described in more detail with reference to.

12 FIG. 12 FIG. 101 251 240 241 242 201 251 242 illustrates a more detailed example of processing of step S. Referring to, the acquiring unitrefers to the memory unitto acquire information included in the positional relationship information, the load information, and the like (step S). The acquiring unitmay acquire weather forecast information and the like, and use the acquired weather forecast information and the like to acquire part of the information included in the load informationand the like.

252 242 251 202 252 The weighting unitapplies a weight to each observation node using the load informationacquired by the acquiring unit(step S). For example, the weighting unitis able to apply a weight using the corresponding load data for the time period for which allocation is made.

253 252 110 150 253 110 203 253 110 110 110 110 204 253 240 205 The allocation unituses the results of weighting by the weighting unitto allocate the observation nodesto each of a plurality of the integration processing nodes. For example, the allocation unituses the results of weighting to select a predetermined number of observation nodesas leader nodes (step S). The allocation unitalso allocates the observation nodesincluded in the system to each of the selected observation nodesthat are leader nodes by, for example, grouping adjacent observation nodesstarting from the selected observation nodethat is a leader node (step S). Thereafter, the allocation unitis able to store the allocation result in the memory unitor the like (step S).

206 200 201 206 200 101 When allocation has not been completed for all periods for which allocation is to be made (NO in step S), the control devicereturns to the processing of step Sand continues allocation. On the other hand, when allocation has been completed for all periods for which allocation is to be made (YES in step S), the control deviceends allocation of integration processing of step S.

101 The above is a more detailed example of processing of step S.

200 251 252 253 253 110 150 252 242 251 150 200 150 As described above, the control deviceincludes the acquiring unit, the weighting unit, and the allocation unit. With this configuration, the allocation unitis able to allocate the observation nodesto each of a plurality of integration processing nodesusing the results of weighting by the weighting unitusing the load informationacquired by the acquiring unit, and the like. As a result, it is possible to prevent the load from being placed unevenly on a specific integration processing node. This allows the control deviceto achieve appropriate distribution when performing integration processing distributed among a plurality of integration processing nodes.

200 150 110 150 150 When the control deviceperforms appropriate distribution, each integration processing nodecan perform integration processing on the appropriately distributed observation nodesas integration targets. As a result, each integration processing nodeis able to improve performance during integration processing. In other words, the use of the method described in the present disclosure can contribute to improvements of the integration processing performance of the integration processing node.

253 253 200 150 The allocation unitis also able to perform allocation for each condition using load data for each condition, such as day of the week or a time period. As a result, the allocation unitis able to perform more appropriate allocations. This allows the control deviceto achieve more appropriate distribution when performing integration processing distributed among a plurality of integration processing nodes.

200 200 200 13 FIG. 3 FIG. 3 FIG. Next, a modification of the control devicedescribed in the first example embodiment will be described with reference to. In the first example embodiment , an example configuration of the control devicehas been described with reference to. However, the configuration of the control deviceis not limited to the case illustrated in.

13 FIG. 13 FIG. 3 FIG. 200 250 200 257 258 244 For example,illustrates another example configuration of the control device. Referring to, the arithmetic processing unitof the control deviceis able to include at least one of a determination unitand an output unitin addition to the configuration illustrated in, by reading and executing the program.

257 254 256 257 257 251 252 253 The determination unituses the most recent information acquired before the instruction by the instruction unit, such as information indicating the load stored in the load information storing unitand weather forecast information, to check whether or not there is a difference between the conditions at the time of previous allocation by the planning unit and the conditions when the instruction is actually given. When the determination unitdetermines that there is a difference, the determination unitinstructs the planning unit configured of the acquiring unit, the weighting unit, and the allocation unitto perform reallocation using the most recent information.

257 253 257 256 110 257 257 257 254 257 257 For example, when the determination unitdetermines that there is a difference between the climate when the planning unit such as the allocation unitperformed allocation in advance and the actual climate, it instructs the planning unit to perform reallocation. Furthermore, when the determination unitdetermines, based on the information indicating the load stored in the load information storing unit, that there is an observation nodewhose load is greater or less than a predetermined value compared with the load when the planning unit performed allocation in advance, the determination unitinstructs the planning unit to perform reallocation. In this way, the determination unitchecks the actual situation to determine whether or not to perform reallocation. Note that, when the determination unitdetermines that reallocation is necessary, the planning unit may perform reallocation, and then the instruction unitmay issue an instruction. Furthermore, the determination unitmay determine whether or not there is a difference using any method. For example, the determination unitmay make the aforementioned determination using an arbitrarily set threshold.

200 257 257 200 150 In this way, the control deviceis able to include the determination unit. With this configuration, the determination unitis able to check whether or not there is a difference between the condition when the planning unit previously performed allocation and the condition when an instruction is actually given, and to instruct reallocation if necessary. As a result, allocation that is more suitable for actual condition can be made when necessary. This allows the control deviceto achieve more appropriate distribution when performing integration processing distributed among a plurality of integration processing nodes.

200 258 258 258 220 230 8 9 FIGS.and As described above, the control deviceis also able to include the output unit. The output unitoutputs information such as those exemplified in. The output unitmay display information on the screen display unitor transmit information to an external device via the communication interface unit.

258 110 150 8 9 FIGS.and For example, the output unitis able to output information such as that illustrated in, which shows the results of allocation made in advance by the planning unit, and the groups and observation nodesthat each integration processing nodeis actually responsible for. By outputting such information, a user checking the output can easily check whether or not distribution has been performed appropriately.

300 200 300 300 300 14 16 FIGS.to 14 FIG. 15 FIG. 16 FIG. Next, an information processing apparatusthat is another modification of the control devicewill be described with reference to.is a diagram illustrating an example of the hardware configuration of the information processing apparatus.is a block diagram illustrating an example configuration of the information processing apparatus.is a flowchart illustrating an example of operation of the information processing apparatus.

300 300 300 14 FIG. 14 FIG. 301 a CPU (Central Processing Unit)(arithmetic unit); 302 a ROM (Read Only Memory)(memory unit); 303 a RAM (Random Access Memory)(memory unit); 304 303 programsloaded into the RAM; 305 304 a storage devicethat stores the programs; 306 310 300 a drive devicethat performs reading from and writing into a storage mediumexternal to the information processing apparatus; 307 311 300 a communication interfaceconnected to a communication networkexternal to the information processing apparatus; 308 an input/output interfacethat performs input/output of data; and 309 a busconnecting the components. The information processing apparatusis an apparatus that allocates observation nodes that perform observations subject to integration processing to a plurality of integration processing nodes that perform integration processing to integrate the observation results by the observation nodes.illustrates an example of the hardware configuration of the information processing apparatus. Referring to, the information processing apparatushas the following hardware configuration, for example:

300 321 322 301 304 304 305 302 303 301 304 301 311 310 306 301 15 FIG. Furthermore, the information processing apparatusis able to realize the functions of the acquiring unitand the allocation unitillustrated inby the CPUacquiring and executing the programs. The programsare stored in advance in the storage deviceor the ROM, for example, and are loaded into the RAMor the like by the CPUfor execution as needed. The programsmay be supplied to the CPUvia the communication network, or may be stored in advance on the storage medium, and the drive devicemay read out the programs and supply them to the CPU.

14 FIG. 300 300 300 306 301 illustrates an example of the hardware configuration of the information processing apparatus. The hardware configuration of the information processing apparatusis not limited to the above-described case. For example, the information processing apparatusmay be configured with only part of the above-described configuration, such as excluding the drive device. Furthermore, the CPUmay be the GPU exemplified in the first example embodiment.

321 321 The acquiring unitacquires load information indicating the load corresponding to each observation node that observes an object. For example, the acquiring unitmay acquire, for each observation node, load information including information indicating the load during integration processing for each predetermined condition.

322 321 322 The allocation unituses the load information acquired by the acquiring unitto allocate the observation nodes that perform observations subject to integration processing to a plurality of integration processing nodes that perform integration processing to integrate the observation results by the observation nodes. For example, the allocation unitmay perform allocation for each predetermined condition.

300 300 16 FIG. The above is an example configuration of the information processing apparatus. Next, an example of operation of the information processing apparatuswill be described with reference to.

16 FIG. 16 FIG. 300 321 301 is a flowchart illustrating an example of operation of the information processing apparatus. Referring to, the acquiring unitacquires load information indicating the load corresponding to each observation node that observes an object (step S).

322 321 302 The allocation unituses the load information acquired by the acquiring unitto allocate the observation nodes that perform observations subject to integration processing to a plurality of integration processing nodes that perform integration processing to integrate the observation results by the observation nodes (step S).

300 321 322 322 321 300 As described above, the information processing apparatusincludes the acquiring unitand the allocation unit. With this configuration, the allocation unitis able to allocate observation nodes to a plurality of integration processing nodes using the load information acquired by the acquiring unit. As a result, it is possible to prevent the load from being unevenly placed on a specific integration processing node. This allows the information processing apparatusto achieve appropriate distribution when performing integration processing distributed among a plurality of integration processing nodes.

300 300 300 The information processing apparatusdescribed above can be realized by installing a predetermined program in an apparatus such as the information processing apparatus. Specifically, a program that is another aspect of the present disclosure is a program for realizing, on an apparatus such as the information processing apparatus, processing to acquire load information indicating the load corresponding to each observation node that observes an object, and with use of the acquired load information, allocate observation nodes that perform observations subject to integration processing to a plurality of integration processing nodes that perform integration processing to integrate observation results by the observation nodes.

300 300 Furthermore, an information processing method, executed by an apparatus such as the information processing apparatusdescribed above, is a method of, by the apparatus such as the information processing apparatus, acquiring load information indicating the load corresponding to each observation node that observes an object, and with use of the acquired load information, allocating the observation nodes that perform observations subject to integration processing to a plurality of integration processing nodes that perform integration processing to integrate observation results by the observation nodes.

Even a program, a computer-readable storage medium having a program stored thereon, or an information processing method, having the above-described configuration, can achieve the same functions and effects as the above-described information processing apparatus 300, and therefore can achieve the above-described object of the present disclosure.

The whole or part of the example embodiments disclosed above can be described as the following supplementary notes. An overview of the information processing apparatus and the like according to the present disclosure will be described below. However, the present disclosure is not limited to the following configuration.

An information processing apparatus comprising:

an acquiring unit that acquires load information indicating a load corresponding to each of observation nodes that observes an object; and

an allocation unit that, with use of the acquired load information, allocates observation nodes that perform observations subject to integration processing to a plurality of integration processing nodes that perform integration processing to integrate observation results by the observation nodes.

The information processing apparatus according to supplementary note 1, wherein

the load information includes, for each of the observation nodes, information indicating a load during the integration processing for each predetermined condition, and

the allocation unit allocates the observation nodes for the each predetermined condition.

The information processing apparatus according to supplementary note 2, wherein

the load information includes, as the information indicating the load during the integration processing, at least one of a number of observations of the object observed previously and an amount of movement indicating an amount by which the object moved from an observation area.

The information processing apparatus according to any one of supplementary notes 1 to 3, further comprising

a weighting unit that performs weighting on each of the observation nodes using the load information, wherein

the allocation unit allocates the observation nodes using a weighting result by the weighting unit.

The information processing apparatus according to any one of supplementary notes 1 to 4, wherein

with use of positional relationship information indicating a positional relationship of the observation nodes, the allocation unit divides the observation nodes into groups according to a number of the integration processing nodes, and allocates the observation nodes by determining an integration processing node responsible for each of the groups.

The information processing apparatus according to supplementary note 3, wherein

the load information includes information indicating at least one of the number of observations of the object and the amount of movement indicating the amount by which the object moved from the observation area, for at least one condition among day of a week, weather, and a time period.

The information processing apparatus according to any one of supplementary notes 4 to 6, wherein

for an observation node in which weighting using the load information is not possible, the weighting unit performs weighting using a weighting result with respect to another observation node that is adjacent to the observation node.

The information processing apparatus according to any one of supplementary notes 1 to 7, wherein

the allocation unit allocates the observation nodes using information indicating a time taken in past integration processing and the load information.

The information processing apparatus according to any one of supplementary notes 1 to 8, further comprising

an instruction unit that instructs each of the integration processing nodes to perform integration processing according to an allocation result by the allocation unit.

9. An information processing method comprising, by an information processing apparatus:

acquiring load information indicating a load corresponding to each of observation nodes that observes an object; and

with use of the acquired load information, allocating observation nodes that perform observations subject to integration processing to a plurality of integration processing nodes that perform integration processing to integrate observation results by the observation nodes.

A program for causing an information processing apparatus to execute processing to:

acquire load information indicating a load corresponding to each of observation nodes that observes an object; and

with use of the acquired load information, allocate observation nodes that perform observations subject to integration processing to a plurality of integration processing nodes that perform integration processing to integrate observation results by the observation nodes.

An integration system comprising:

an information processing apparatus including an acquiring unit that acquires load information indicating a load corresponding to each of observation nodes that observes an object, and an allocation unit that, with use of the acquired load information, allocates observation nodes that perform observations subject to integration processing to a plurality of integration processing nodes that perform integration processing to integrate observation results by the observation nodes; and

the plurality of integration processing nodes that perform integration processing to integrate observation results by the observation nodes allocated by the information processing apparatus.

Note that some or all of the configurations described in Supplementary Notes 2 to 8-1 that are dependent on the information processing apparatus described as Supplementary Note 1 may also be dependent in a similar dependent relationship on the information processing method described in Supplementary Note 9, the program described in Supplementary Note 10, the integrated system described in Supplementary Note 10-1, and the like. Furthermore, not limited to Supplementary Notes 9, 10, and 10-1, some or all of the configurations described as supplementary notes may also be dependent on various hardware, software, and various storage means for storage software, methods, programs, or systems within the scope of each of the above-mentioned example embodiments.

Furthermore, the programs described in the example embodiments and supplementary notes described above can be stored in and supplied to a computer using various types of non-transitory computer readable media. Non-transitory computer-readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic storage media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical storage media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R/Ws, and semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs (Random Access Memory)). In addition, a program may be provided to a computer by various types of transitory computer-readable media. Examples of transitory computer-readable media include electrical signals, optical signals, and electromagnetic waves. A transitory computer-readable medium may provide a program to the computer via a wired communication channel, such as an electric wire and an optical fiber, or a wireless communication channel.

Although the present disclosure has been described above with reference to the above-described example embodiments, the present disclosure is not limited to the above-described example embodiments. The configurations and details of the present disclosure can be changed in a variety of ways that those skilled in the art can understand within the scope of the present disclosure. Each embodiment can be combined with other embodiments as appropriate.

100 data integration system

110 observation node

120 analysis device

130 analysis data storage device

140 integrated data storage device

150 integration processing node

200 control device

210 operation input unit

220 screen display unit

230 communication interface unit

240 memory unit

241 positional relationship information

242 load information

243 processing time information

244 program

250 arithmetic processing unit

251 acquiring unit

252 weighting unit

253 allocation unit

254 instruction unit

255 processing time storing unit

256 load information storing unit

257 determination unit

258 output unit

300 information processing apparatus

301 CPU

302 ROM

303 RAM

304 programs

305 storage device

306 drive device

307 communication interface

308 input/output interface

309 bus

310 storage medium

311 communication network

321 acquiring unit

322 allocation unit